Home ownership by race
# get tidy tuesday data for the date 2021-02-09
# tuesdata <- tidytuesdayR::tt_load('2021-02-09')
# get home_ownership data
# tuesdata$home_owner %>%
# write_csv(file = here::here('2021-02-09', 'home_owner.csv'))
home_owner <- read_csv(here::here('2021-02-09', 'home_owner.csv'))
# The color-blind-friendly palette with black:
cbPalette <- c("#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
# y coordinates for annotating lines
y_coord <- home_owner %>%
filter(year == max(year))
# create the plot
home_owner_plot <- home_owner %>%
ggplot(mapping = aes(x = year, y = home_owner_pct)) +
geom_line(mapping = aes(color = race),
size = 0.8) +
geom_point(mapping = aes(color = race),
shape = 21,
fill = 'white') +
labs(x = 'Year',
y = '',
title = 'Home ownership in the US from 1976 to 2016') +
scale_color_manual(values = cbPalette[2:4]) +
theme_bw(base_size = 14) +
xlim(1975, 2021) +
annotate('text', x = 2019, y = filter(y_coord, race == 'White')$home_owner_pct,
label = 'White', color = cbPalette[4]) +
annotate('text', x = 2019, y = filter(y_coord, race == 'Black')$home_owner_pct,
label = 'Black', color = cbPalette[2]) +
annotate('text', x = 2019, y = filter(y_coord, race == 'Hispanic')$home_owner_pct,
label = 'Hispanic', color = cbPalette[3]) +
theme(legend.position = 'none',
panel.border = element_blank(),
panel.grid.minor = element_blank(),
plot.title = element_text(hjust = 0.5)) +
scale_y_continuous(labels = scales::percent, limits = c(0, 1))
ggplotly(home_owner_plot)
# ggsave(here::here('2021-02-09/plots', 'home_ownership.png'),
# plot = home_owner_plot,
# width = 8,
# height = 5)
# htmlwidgets::saveWidget(as_widget(ggplotly(home_owner_plot)),
# here::here('2021-02-09/plots', 'home-ownership-interactive.html'))